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SciML / DiffEqParamEstim.jl

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
https://docs.sciml.ai/DiffEqParamEstim/stable/
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two stage method hyperparameters #173

Open ArnoStrouwen opened 2 years ago

ArnoStrouwen commented 2 years ago

Could it be documented if/how the width and weights of the kernels are adapted based on the frequency of the data? In particular if the time between measurements is not equal.

e.g. where things like https://github.com/SciML/DiffEqParamEstim.jl/blob/5866c3238f9e65320e41054f9617535c4be54a72/src/two_stage_method.jl#L56 come from

ChrisRackauckas commented 2 years ago

yes, that would be nice to add.

sathvikbhagavan commented 1 year ago

@ChrisRackauckas, do you know from where the formula for bandwidth is taken? I am unable to locate the source.

ChrisRackauckas commented 1 year ago

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2631937/